Image splicing method

ABSTRACT

Provided is an image splicing method, including the following. A to-be-predicted image is divided into first and second cropped images. An overlap region exists between the first and second cropped images. The overlap region is divided into first and second sub-overlap regions. A first image region that does not include the second sub-overlap region is found in the first cropped image, and a second image region that does not include the first sub-overlap region is found in the second cropped image. First and second prediction result images respectively corresponding to the first and second cropped images are generated. A first prediction image region corresponding to the first image region is found in the first prediction result image, and a second prediction image region corresponding to the second image region is found in the second prediction result image. The first and second prediction image regions are spliced into a spliced image.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of Taiwanese applicationNo. 110113066, filed on Apr. 12, 2021. The entirety of theabove-mentioned patent application is hereby incorporated by referenceherein and made a part of this specification.

BACKGROUND Technical Field

The disclosure relates to an image processing technology; particularly,the disclosure relates to an image splicing method.

Description of Related Art

Currently, in fields related to smart healthcare, it is often requiredto establish a system that assists medical doctors with identification,judgment, or evaluation on medical images through deep learning.However, data set for training often has characteristics of smallquantity, high resolution, and presence of numerous identical objects inone single medical image. Therefore, during the training phase, medicalimages are often divided to increase the amount of training data in thetraining data set.

Generally speaking, after division and prediction, the medical imagesstill need to be spliced back into the original image size. In addition,the splicing process usually needs to be completed through some complexalgorithms. Moreover, without a proper design of the splicing process,serious distortion may be present in the spliced image.

Therefore, for those skilled in the art, how to design a simple imagesplicing mechanism with low distortion is an issue to work on.

SUMMARY

The disclosure provides an image splicing method, which may solve theabove technical problem.

The disclosure provides an image splicing method, adapted for an imageprocessing device. The method includes the following. A to-be-predictedimage is obtained, and the to-be-predicted image is divided into atleast a first cropped image and a second cropped image. A first overlapregion exists between the first cropped image and the second croppedimage. The first overlap region is divided into a first sub-overlapregion closer to the first cropped image and a second sub-overlap regioncloser to the second cropped image. A first image region that does notincludes the second sub-overlap region is found in the first croppedimage, and a second image region that does not include the firstsub-overlap region is found in the second cropped image. A predictionoperation is individually performed on the first cropped image and thesecond cropped image to generate a first prediction result image and asecond prediction result image respectively corresponding to the firstcropped image and the second cropped image. A first prediction imageregion corresponding to the first image region is found in the firstprediction result image, and a second prediction image regioncorresponding to the second image region is found in the secondprediction result image. At least the first prediction image region andthe second prediction image region are spliced into a spliced image. Afirst relative position between the first prediction image region andthe second prediction image region in the spliced image corresponds to asecond relative position between the first image region and the secondimage region in the to-be-predicted image.

To make the aforementioned more comprehensible, several embodimentsaccompanied with drawings are described in detail as follows.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of the disclosure, and are incorporated in and constitutea part of this specification. The drawings illustrate exemplaryembodiments of the disclosure and, together with the description, serveto explain the principles of the disclosure.

FIG. 1 is a schematic diagram of an image processing device according toan embodiment of the disclosure.

FIG. 2 is a schematic diagram showing a to-be-predicted image dividedaccording to an embodiment of the disclosure.

FIG. 3 is a comparison diagram of a spliced image according to anembodiment of the disclosure.

FIG. 4 is a flowchart of an image splicing method according to anembodiment of the disclosure.

FIG. 5 is a diagram showing an application scenario according to anembodiment of the disclosure.

FIG. 6A to FIG. 6C are schematic diagrams of a plurality of predictionscenarios according to FIG. 5 .

FIG. 7A to FIG. 7D are diagrams of a plurality of application scenariosaccording to FIG. 2 .

FIG. 8 is a comparison diagram of a spliced image according to in FIG. 3.

DESCRIPTION OF THE EMBODIMENTS

With reference to FIG. 1 , which is a schematic diagram of an imageprocessing device according to an embodiment of the disclosure, indifferent embodiments, the image processing device 100 includes, forexample but not limited to, various servers, computer devices, and/orsmart devices.

As shown in FIG. 1 , an image processing device 100 may include astorage circuit 102 and a processor 104. The storage circuit 102 is, forexample, any type of fixed or mobile random access memory (RAM),read-only memory (ROM), flash memory, hard disk or other similar devicesor a combination of these devices, and may be configured to record aplurality of programming codes or modules.

The processor 104 is coupled to the storage circuit 102, and may be ageneral-purpose processor, a special-purpose processor, a conventionalprocessor, a digital signal processor, a plurality of microprocessors,one or more microprocessors combined with a digital signal processorcore, a controller, a microcontroller, an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA), anyother integrated circuit, a state machine, a processor based on AdvancedRISC Machine (ARM) and the like.

In the embodiment of the disclosure, the processor 104 may access themodules or programming codes recorded in the storage circuit 102 torealize the image splicing method proposed by the disclosure, which willbe described in detail as follows.

Since medical images are of high privacy, high difficulty of labeling,and a relatively small number of cases, the number of data sets tend tobe relatively small in data collection. In cell culture, takingcalculation of area coverage of cells in a culture medium as an example,since the cells in a single image are of a great number and labelingthereof is time-consuming, it is relatively difficult to obtain a greatnumber of data sets. However, because of the multitude of cells in asingle image, if the image is divided into several smaller images fortraining and prediction, it should be able to alleviate theinsufficiency of data in the data set.

Generally speaking, mainly in a deep learning mechanism, variousfeatures in a screen are captured, each combined with a weight value. Inaddition, an optimal weight ratio is found through the training data setwith correct answers marked in advance to accordingly perform aprediction on a to-be-predicted image lastly.

However, it is inevitable that, during image division, some objects thatcould have been predicted becomes incomplete as a result of the imagedivision and thus cannot be detected. Therefore, the division is usuallyperformed so that an overlap region exists between each of the dividedimages. In this way, detection of each to-be-predicted object may beensured to the maximum extent.

With reference to FIG. 2 , which is a schematic diagram showing ato-be-predicted image divided according to an embodiment of thedisclosure, a to-be-predicted image 200 in FIG. 2 is, for example butnot limited to, the aforementioned medical image or another image to bedivided to augment the training data.

As shown in FIG. 2 , the to-be-predicted image 200 may include aplurality of to-be-predicted objects OB. In this embodiment, theto-be-predicted image 200 is an 800×600 grayscale image. The imageprocessing device 100 needs to divide the to-be-predicted image 200 intoa plurality of W×H cropped images (W and H are respectively the widthand height of each cropped image).

In an embodiment, when the processor 104 obtains the to-be-predictedimage 200, a W×H rectangular window may first be established taking anupper left corner of the to-be-predicted image 200 (coordinates being(0, 0), for example) as a reference point. In addition, an image regioncovered by the rectangular window is taken as a first cropped image IM1.

After that, the processor 104 may shift the rectangular window rightwardby a horizontal movement range (referred to as CW), and an image regioncovered by the shifted rectangular window is taken as a second croppedimage IM2. In different embodiments, CW may be set to be smaller thanthe pixel number of W depending on the needs of the designer. Inaddition, in the case of FIG. 2 , assuming that coordinates of an upperleft corner pixel of the first cropped image IM1 are represented as(x_(a), y_(a)), and coordinates of an upper left corner pixel of thesecond cropped image IM2 are represented as (x_(b),y_(b)), then it canbe known that y_(a)=y_(b) and x_(b)−x_(a)=CW. Nonetheless, thedisclosure is not limited thereto.

In other embodiments, the processor 104 may continue to shift therectangular window rightward to find other cropped images on the rightside of the second cropped image IM2. In addition, although the firstcropped image IM1 and the second cropped image IM2 have the same size, asize (width, in particular) of the rightmost cropped image may besmaller than those of the other cropped images (e.g., the first croppedimage IM1 and the second cropped image IM2) with the same height.

In some embodiment, for the rightmost cropped image to serve forsubsequent prediction operation, the processor 104 may augment therightmost cropped image to the same size as the first cropped image IM1,and the augmented part may be filled with a specific value (e.g., 0) bythe processor 104. Nonetheless, the disclosure is not limited thereto.For other cropped images with a smaller size, the processor 104 may alsoperform the augmentation to adjust the cropped images to the same sizeas the first cropped image IM1. Nonetheless, the disclosure is notlimited thereto.

In addition, after the processor 104 finds a cropped image with the sameheight as the first cropped image IM1, the rectangular window may firstbe restored to a position corresponding to the first cropped image IM1,then the mentioned rectangular window may be moved vertically downwardby a vertical movement range (referred to as CH), and an image regioncovered by the rectangular window moved downward may be taken as a thirdcropped image IM3. In different embodiments, CH may be set to be smallerthan the pixel number of H depending on the needs of the designer. Inaddition, in the case of FIG. 2 , assuming that the coordinates of theupper left corner pixel of the first cropped image IM1 are representedas (x_(a), y_(a)), and coordinates of an upper left corner pixel of thethird cropped image IM3 are represented as (x_(c),y_(c)), then it can beknown that x_(a)=x_(c) and y_(c)−y_(a)=CH. Nonetheless, the disclosureis not limited thereto.

In other embodiments, the processor 104 may continue to shift therectangular window CW rightward to find other cropped images on theright side of the third cropped image IM3.

After the processor 104 finds a cropped image with the same height asthe third cropped image IM3, the rectangular window may first berestored to a position corresponding to the third cropped image IM3,then the rectangular window may be moved vertically downward by CH toobtain other cropped images below the third cropped image IM3. The abovemechanism may be continually repeated until the to-be-predicted image200 is completely divided into a plurality of cropped images.Nonetheless, the disclosure is not limited thereto.

As shown in FIG. 2 , when CW is smaller than W and CH is smaller than H,an overlap region exists between adjacent cropped images. For example, afirst overlap region OR1 exists between the first cropped image IM1 andthe second cropped image IM2, and a second overlap region OR2 existsbetween the first cropped image IM1 and the third cropped image IM3.

After that, the processor 104 may, for example, perform a predictionoperation on each cropped image of the to-be-predicted image 200 togenerate a prediction result image corresponding to each cropped image.

In an embodiment, the prediction result image corresponding to eachcropped image is, for example, a binary image. For example, in eachprediction result image, a pixel corresponding to the to-be-predictedobjects OB may be represented as a first value (e.g., 1), and a pixelnot corresponding to the to-be-predicted object OB may be represented asa second value (e.g., 0). Nonetheless, the disclosure is not limitedthereto.

Since an overlap region exists between adjacent cropped images, afterthe prediction result image of each cropped image is obtained, a seriesof complex calculations, data search and matching are usually requiredto confirm correctness of each detectable object in the overlap region.In this case, it takes more time to splice each prediction result image.However, if the overlap region is directly overwritten without matching,a corresponding spliced image is prone to errors occurring at placescorresponding to boundaries between the cropped images.

Reference may be made to FIG. 3 , which is a comparison diagram of aspliced image according to an embodiment of the disclosure. In FIG. 3 ,a spliced image 311 is, for example, a splicing result generated bydirectly overwriting the overlap region, and a spliced image 312 is, forexample, the correct splicing result. As shown by the spliced image 311and the spliced image 312, many places (e.g., circled parts) are presentin the spliced image 311 that are different in the spliced image 312.Accordingly, without a properly designed mechanism for splicing theprediction result images, distortion may be present in the splicingresult.

In view of this, the disclosure proposes an image splicing method, whichmay solve the above problem.

With reference to FIG. 4 , which is a flowchart of an image splicingmethod according to an embodiment of the disclosure, the method in thisembodiment may be executed by the image processing device 100 of FIG. 1. Hereinafter, details of each step of FIG. 4 will be described inaccompany with the elements shown in FIG. 1 . In addition, to make theconcept in the disclosure more comprehensible, the application scenarioshown in FIG. 5 is adopted for further description in the following.

First, in step S410, the processor 104 may obtain a to-be-predictedimage 510, and divide the to-be-predicted image 510 into at least afirst cropped image 511 and a second cropped image 512. For ease ofdescription, it is assumed in FIG. 5 that the to-be-predicted image 510is divided into two cropped images of equal size (i.e., the firstcropped image 511 and the second cropped image 512), but the disclosureis not limited thereto. In addition, as shown in FIG. 5 , a firstoverlap region 520 exists between the first cropped image 511 and thesecond cropped image 512.

In FIG. 5 , the to-be-predicted image 510 may, for example, include aplurality of to-be-predicted objects (not shown). By appropriatelyselecting CW and W, among other parameters, a width of the first overlapregion 520 (which is a rectangular region, for example) may be adjustedto a predetermined multiple (e.g., 3 times) of an average width of theto-be-predicted objects. Nonetheless, the disclosure is not limitedthereto.

After that, in step S420, the processor 104 may divide the first overlapregion 520 into a first sub-overlap region 521 closer to the firstcropped image 511 and a second sub-overlap region 522 closer to thesecond cropped image 512.

In FIG. 5 , the processor 104 may, for example, define a half of thefirst overlap region 520 that is closer to the first cropped image 511as the first sub-overlap region 521, and define another half of thefirst overlap region 520 that is closer to the second cropped image 512as the second sub-overlap region 522. In other embodiments, theprocessor 104 may also define a first sub-overlap region and a secondsub-overlap region in the first overlap region 520 in other mannersdepending on the needs of the designer.

Then, in step S430, the processor 104 may find a first image region 511a that does not include the second sub-overlap region 522 in the firstcropped image 511, and find a second image region 512 a that does notinclude the first sub-overlap region 521 in the second cropped image512.

In FIG. 5 , the first image region 511 a is, for example, an imageregion remaining after the second sub-overlap region 522 is removed fromthe first cropped image 511, and the second image region 512 a is, forexample, an image region remaining after the first sub-overlap region521 is removed from the second cropped image 512. Nonetheless, thedisclosure is not limited thereto.

In step S440, the processor 104 may individually perform a predictionoperation on the first cropped image 511 and the second cropped image512 to generate a first prediction result image 531 and a secondprediction result image 532 respectively corresponding to the firstcropped image 511 and the second cropped image 512.

In an embodiment, the processor 104 may, for example, input the firstcropped image 511 into a pre-trained neural network to generate thefirst prediction result image 531 through the neural network. Forexample, after the neural network identifies one or more to-be-predictedobjects in the first cropped image 511, in the first prediction resultimage 531, the neural network may represent pixels corresponding to theto-be-predicted objects using a first value, and represent pixels notcorresponding to the to-be-predicted objects using a second value.Nonetheless, the disclosure is not limited thereto. Similarly, theprocessor 104 may input the second cropped image 512 into the neuralnetwork to generate the second prediction result image 532 through theneural network, of which details will not be repeatedly describedherein.

In step S450, the processor 104 may find a first prediction image region531 a corresponding to the first image region 511 a in the firstprediction result image 531, and find a second prediction image region532 a corresponding to the second image region 512 a in the secondprediction result image 532.

After that, in step S460, the processor 104 may splice at least thefirst prediction image region 531 a and the second prediction imageregion 532 a into a spliced image 540. A first relative position betweenthe first prediction image region 531 a and the second prediction imageregion 532 a in the spliced image 540 corresponds to a second relativeposition between the first image region 511 a and the second imageregion 512 a in the to-be-predicted image 510.

Specifically, in the to-be-predicted image 510, since the first imageregion 511 a is closely adjacent to the second image region 512 a on theleft side (i.e., the second relative position), in the spliced image540, the first prediction image region 531 a is also closely adjacent tothe second prediction image region 532 a on the left side (i.e., thefirst relative position).

From another point of view, the above step may be interpreted asrespectively removing the parts (i.e., the regions illustrated withslanted lines) corresponding to the second sub-overlap region 522 andthe first sub-overlap region 521 from the first prediction result image531 and the second prediction result image 532, and splicing theremaining parts in the first prediction result image 531 and the secondprediction result image 532 into the spliced image 540. Thereby, thespliced image 540 has relatively low distortion.

With reference to FIG. 6A to FIG. 6C, which are schematic diagrams of aplurality of prediction scenarios according to FIG. 5 , it is assumed inFIG. 6A that only part of the to-be-predicted object OB is located inthe first cropped image 511. In this case, since the to-be-predictedobject OB is not completely presented in the first cropped image 511, itis possible that the neural network fails to identify theto-be-predicted object OB in the first cropped image 511, andcorrespondingly, the to-be-predicted object OB is also not presented inthe first prediction result image 531.

However, since the to-be-predicted object OB is completely presented inthe second cropped image 512, the neural network should be able tosuccessfully identify the to-be-predicted object OB in the secondcropped image 512. Correspondingly, the to-be-predicted object OB may bepresented in the second prediction result image 532. Therefore, aftersteps S450 and S460 are executed, information of the to-be-predictedobject OB is correspondingly retained in the generated spliced image540.

In FIG. 6B, it is assumed that only part of the to-be-predicted objectOB is located in the second cropped image 512. In this case, since theto-be-predicted object OB is not completely presented in the secondcropped image 512, it is possible that the neural network fails toidentify the to-be-predicted object OB in the second cropped image 512,and correspondingly, the to-be-predicted object OB is also not presentedin the second prediction result image 532.

However, since the to-be-predicted object OB is completely presented inthe first cropped image 511, the neural network should be able tosuccessfully identify the to-be-predicted object OB in the first croppedimage 511. Correspondingly, the to-be-predicted object OB may bepresented in the first prediction result image 531. Therefore, aftersteps S450 and S460 are executed, the information of the to-be-predictedobject OB is correspondingly retained in the generated spliced image540.

In FIG. 6C, it is assumed that the to-be-predicted object OB is locatedin the middle of the first overlap region 520. In this case, since theto-be-predicted object OB is presented relatively completely in thefirst cropped image 511 and the second cropped image 512, the neuralnetwork should be able to successfully identify the to-be-predictedobject OB in the first cropped image 511 and the second cropped image512. Correspondingly, the to-be-predicted object OB may be presented inboth the first prediction result image 531 and the second predictionresult image 532. Therefore, after steps S450 and S460 are executed, theinformation of the to-be-predicted object OB is correspondingly retainedin the generated spliced image 540.

Reference may be made to FIG. 7A to FIG. 7D, which are diagrams of aplurality of application scenarios according to FIG. 2 . In FIG. 7A,after the to-be-predicted image 200 is divided into a plurality ofcropped images, the processor 104 may find a required image region ineach cropped image based on the relevant teaching of FIG. 4 and FIG. 5 ,and accordingly find a prediction image region for generating a splicedimage in a corresponding prediction result image.

In FIG. 7A, the processor 104 may divide the first overlap region OR1between the first cropped image IM1 and the second cropped image IM2into a first sub-overlap region OR11 and a second sub-overlap regionOR12. In addition, since the second overlap region OR2 exists betweenthe first cropped image IM1 and the third cropped image IM3, theprocessor 104 may divide the second overlap region OR2 into a thirdsub-overlap region OR21 and a fourth sub-overlap region OR22.

Therefore, after a first image region IM1 a in the first cropped imageIM1 is determined based on the first cropped image IM1 and the secondcropped image IM2, the processor 104 may further remove the fourthsub-overlap region OR22 from the first image region IM1 a to form thefirst image region IM1 a shown in FIG. 7A.

Based on the above, after generating a first prediction result imagecorresponding to the first cropped image IM1, the processor 104 findsthe first prediction image region adapted to form the spliced image inthe first prediction result image according to the first image regionIM1 a shown in FIG. 7A.

In FIG. 7B, since other cropped images also exists on the right side ofand below the second cropped image IM2, the processor 104 may find theoverlap regions between the second cropped image IM2 and the surroundingcropped images (i.e., the first cropped image IM1, a fourth croppedimage IM4, and a fifth cropped image IM5) based on the above teachings,and remove parts of the overlap regions closer to the surroundingcropped images from the second cropped image IM2. Thereby, a secondimage region IM2 a shown in FIG. 7B is formed.

After generating a second prediction result image corresponding to thesecond cropped image IM2, the processor 104 may find the secondprediction image region adapted to form the spliced image in the secondprediction result image according to the second image region IM2 a shownin FIG. 7B.

In addition, for the third image IM3, the processor 104 may also findthe overlap regions between the third cropped image IM3 and thesurrounding cropped images (e.g., the first cropped image IM1, thesecond cropped image IM2, etc.) based on the above mechanism, and removeparts of the overlap regions closer to the surrounding cropped imagesfrom the third cropped image IM3. Thereby, a third image region IM3 acorresponding to the third cropped image IM3 is formed.

After generating a third prediction result image corresponding to thethird cropped image IM3, the processor 104 may find the third predictionimage region adapted to form the spliced image in the third predictionresult image according to the third image region IM3 a.

In FIG. 7C, based on the above mechanism, the processor 104 may alsofind the overlap regions between the fourth cropped image IM4 and thesurrounding cropped images (e.g., the first cropped image IM1, thesecond cropped image IM2, the third cropped image IM3, the fifth croppedimage IM5, etc.), and remove parts of the overlap regions closer to thesurrounding cropped images from the fourth cropped image IM4. Thereby, afourth image region IM4 a corresponding to the fourth cropped image IM4is formed.

After generating a fourth prediction result image corresponding to thefourth cropped image IM4, the processor 104 may find the fourthprediction image region adapted to form the spliced image in the fourthprediction result image according to the fourth image region IM4 a.

After the above operations are performed on each of the cropped imagesin the to-be-predicted image 200, the image regions corresponding to thecropped images may be presented as each of areas illustrated with brokenline as shown in FIG. 7D. Based on this, after obtaining the predictionresult images corresponding to the cropped images, the processor 104 mayfind the required prediction image regions in each of the predictionresult images according to FIG. 7D to be spliced to form the requiredspliced image. Thereby, the information of each to-be-predicted objectOB is retained in the spliced image with low distortion.

With reference to FIG. 8 , which is a comparison diagram of a splicedimage according to in FIG. 3 , a spliced image 811 in FIG. 8 is, forexample, a splicing result generated by the method of the disclosure. Asshown by the spliced image 311, the spliced image 312, and the splicedimage 811, the spliced image 811 is more similar to the spliced image312 than the spliced image 311.

In summary of the foregoing, in the disclosure, since the image regionwhere the information of the to-be-predicted object may be retained isfound in each of the cropped images, and accordingly the predictionimage regions are found in the prediction result images corresponding tothe cropped images, the information of each to-be-predicted objects isrelatively accurately presented in the spliced image formed by splicingthe prediction image regions, thereby reducing distortion caused bysplicing.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the disclosed embodimentswithout departing from the scope or spirit of the disclosure. In view ofthe foregoing, it is intended that the disclosure covers modificationsand variations provided that they fall within the scope of the followingclaims and their equivalents.

What is claimed is:
 1. An image splicing method adapted for an imageprocessing device, the method comprising: obtaining a to-be-predictedimage, and dividing the to-be-predicted image into at least a firstcropped image and a second cropped image, wherein a first overlap regionexists between the first cropped image and the second cropped image,wherein coordinates of an upper left corner pixel of the first croppedimage in the to-be-predicted image are (x_(a),y) and coordinates of anupper left corner pixel of the second cropped image in theto-be-predicted image is (x_(b),y_(b)), where y_(a)=y_(b),x_(b)−x_(a)=CW, and CW is a horizontal movement range; dividing thefirst overlap region into a first sub-overlap region and a secondsub-overlap region, wherein the first overlap region is closer to thefirst cropped image than the second cropped image, and the secondsub-overlap region is closer to the second cropped image than the firstcropped image; finding, in the first cropped image, a first image regionnot comprising the second sub-overlap region in the first cropped image,and finding, in the second cropped image, a second image region notcomprising the first sub-overlap region in the second cropped image;individually performing a prediction operation on the first croppedimage and the second cropped image to generate a first prediction resultimage and a second prediction result image respectively corresponding tothe first cropped image and the second cropped image; finding a firstprediction image region corresponding to the first image region in thefirst prediction result image, and finding a second prediction imageregion corresponding to the second image region in the second predictionresult image; and splicing at least the first prediction image regionand the second prediction image region into a spliced image, wherein afirst relative position between the first prediction image region andthe second prediction image region in the spliced image corresponds to asecond relative position between the first image region and the secondimage region in the to-be-predicted image.
 2. The method as described inclaim 1, wherein the first overlap region is a rectangular region, theto-be-predicted image comprises at least one to-be-predicted object, theat least one to-be-predicted object has an average width, and a width ofthe first overlap region is at least a predetermined multiple of theaverage width.
 3. The method as described in claim 1, wherein the stepof dividing the first overlap region into the first sub-overlap regioncloser to the first cropped image and the second sub-overlap regioncloser to the second cropped image comprises: defining a half of thefirst overlap region closer to the first cropped image as the firstsub-overlap region, and defining another half of the first overlapregion closer to the second cropped image as the second sub-overlapregion.
 4. The method as described in claim 1, wherein theto-be-predicted image comprises at least one to-be-predicted object, apixel corresponding to the at least one to-be-predicted object in thefirst prediction result image and the second prediction result image isset to a first value, and a pixel non-corresponding to the at least oneto-be-predicted object in the first prediction result image and thesecond prediction result image is set to a second value.
 5. The methodas described in claim 1, wherein the to-be-predicted image is furtherdivided into a third cropped image, a second overlap region existsbetween the first cropped image and the third cropped image, and themethod further comprises: dividing the second overlap region into athird sub-overlap region closer to the first cropped image and a fourthsub-overlap region closer to the third cropped image, and removing thefourth sub-overlap region from the first image region; finding a thirdimage region not comprising the third sub-overlap region in the thirdcropped image; performing a prediction operation on the third croppedimage to generate a third prediction result image corresponding to thethird cropped image; finding a third prediction image regioncorresponding to the third image region in the third prediction resultimage; and generating the spliced image based on at least the firstprediction image region, the second prediction image region, and thethird prediction image region, wherein a third relative position betweenthe first prediction image region and the third prediction image regionin the spliced image corresponds to a fourth relative position betweenthe first image region and the third image region in the to-be-predictedimage.
 6. The method as described in claim 5, wherein coordinates of anupper left corner pixel of the third cropped image in theto-be-predicted image are (x_(c), y_(c)), where x_(a)=x_(c),y_(c)−y_(a)=CH, and CH is a vertical movement range.